"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class  snake_case__      (       snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					int							  =						StableDiffusionXLImgaImgPipeline
 _snake_case       :					List[str]							  =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
 _snake_case       :					int							  =						PipelineTesterMixin.required_optional_params - {"""latents"""}
 _snake_case       :					Any							  =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 _snake_case       :					Dict							  =						IMAGE_TO_IMAGE_IMAGE_PARAMS
 _snake_case       :					List[str]							  =						IMAGE_TO_IMAGE_IMAGE_PARAMS


 def 						a__   ( self						):
        torch.manual_seed(0						)
        __a       =    UNetaDConditionModel(
            block_out_channels=(32, 64)    ,		layers_per_block=2    ,		sample_size=32    ,		in_channels=4    ,		out_channels=4    ,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,		up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,		attention_head_dim=(2, 4)    ,		use_linear_projection=lowerCamelCase    ,		addition_embed_type="text_time"    ,		addition_time_embed_dim=8    ,		transformer_layers_per_block=(1, 2)    ,		projection_class_embeddings_input_dim=80    ,		cross_attention_dim=64    ,		)
        __a       =    EulerDiscreteScheduler(
            beta_start=0.0_0085    ,		beta_end=0.012    ,		steps_offset=1    ,		beta_schedule="scaled_linear"    ,		timestep_spacing="leading"    ,		)
        torch.manual_seed(0						)
        __a       =    AutoencoderKL(
            block_out_channels=[32, 64]    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,		up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,		latent_channels=4    ,		sample_size=128    ,		)
        torch.manual_seed(0						)
        __a       =    CLIPTextConfig(
            bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=32    ,		intermediate_size=37    ,		layer_norm_eps=1E-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=1000    ,		hidden_act="gelu"    ,		projection_dim=32    ,		)
        __a       =    CLIPTextModel(lowerCamelCase						)
        __a       =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    ,		local_files_only=lowerCamelCase						)

        __a       =    CLIPTextModelWithProjection(lowerCamelCase						)
        __a       =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    ,		local_files_only=lowerCamelCase						)

        __a       =    {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_a,
            "tokenizer_2": tokenizer_a,
            # "safety_checker": None,
            # "feature_extractor": None,
        }
        return components


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=0						):
        __a       =    floats_tensor((1, 3, 32, 32)    ,		rng=random.Random(lowerCamelCase						)						).to(lowerCamelCase						)
        __a       =    image / 2 + 0.5
        if str(lowerCamelCase						).startswith("mps"						):
               __a       =    torch.manual_seed(lowerCamelCase						)
        else:
               __a       =    torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
        __a       =    {
            "prompt": "A painting of a squirrel eating a burger",
            "image": image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 5.0,
            "output_type": "numpy",
            "strength": 0.75,
        }
        return inputs


 def 						a__   ( self						):
        __a       =    "cpu"  # ensure determinism for the device-dependent torch.Generator
        __a       =    self.get_dummy_components()
        __a       =    StableDiffusionXLImgaImgPipeline(**lowerCamelCase						)
        __a       =    sd_pipe.to(lowerCamelCase						)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase						)

        __a       =    self.get_dummy_inputs(lowerCamelCase						)
        __a       =    sd_pipe(**lowerCamelCase						).images
        __a       =    image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)

        __a       =    np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]						)

        assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


 def 						a__   ( self						):
        super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)


 def 						a__   ( self						):
        super().test_inference_batch_single_identical(expected_max_diff=3E-3						)


 def 						a__   ( self						):
        pass






 def 						a__   ( self						):
        __a       =    self.get_dummy_components()
        __a       =    StableDiffusionXLImgaImgPipeline(**lowerCamelCase						)
        __a       =    sd_pipe.to(lowerCamelCase						)
        __a       =    sd_pipe.to(lowerCamelCase						)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase						)

        # forward without prompt embeds
        __a       =    self.get_dummy_inputs(lowerCamelCase						)
        __a       =    3 * ["this is a negative prompt"]
        __a       =    negative_prompt
        __a       =    3 * [inputs["prompt"]]

        __a       =    sd_pipe(**lowerCamelCase						)
        __a       =    output.images[0, -3:, -3:, -1]

        # forward with prompt embeds
        __a       =    self.get_dummy_inputs(lowerCamelCase						)
        __a       =    3 * ["this is a negative prompt"]
        __a       =    3 * [inputs.pop("prompt"						)]

        (
            (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   
        )       =    sd_pipe.encode_prompt(lowerCamelCase    ,		negative_prompt=lowerCamelCase						)

        __a       =    sd_pipe(
            **lowerCamelCase    ,		prompt_embeds=lowerCamelCase    ,		negative_prompt_embeds=lowerCamelCase    ,		pooled_prompt_embeds=lowerCamelCase    ,		negative_pooled_prompt_embeds=lowerCamelCase    ,		)
        __a       =    output.images[0, -3:, -3:, -1]

        # make sure that it's equal
        assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()						).max() < 1E-4






@slow
@require_torch_gpu
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase="cpu"    ,		lowerCamelCase=torch.floataa    ,		lowerCamelCase=0						):
        __a       =    torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
        __a       =    np.random.RandomState(lowerCamelCase						).standard_normal((1, 4, 64, 64)						)
        __a       =    torch.from_numpy(lowerCamelCase						).to(device=lowerCamelCase    ,		dtype=lowerCamelCase						)
        __a       =    {
            "prompt": "a photograph of an astronaut riding a horse",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs






 def 						a__   ( self						):
        __a       =    DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"						)
        pipe.to(lowerCamelCase						)
        pipe.set_progress_bar_config(disable=lowerCamelCase						)

        __a       =    self.get_inputs(lowerCamelCase						)
        __a       =    pipe(**lowerCamelCase						).images
        __a       =    image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        __a       =    np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]						)
        assert np.abs(image_slice - expected_slice						).max() < 7E-3

"""simple docstring"""



from __future__ import annotations

import math



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    u
       for i in range(1   ,    a				):
              __a       =    temp * (u - i)
       return temp



def 	_lowerCamelCase(	):
       __a       =    int(input("enter the numbers of values: "				)				)
       __a       =    []
       for _ in range(a				):
              y.append([]				)
       for i in range(a				):
              for j in range(a				):
                     y[i].append(a				)
                     __a       =    0

       print("enter the values of parameters in a list: "				)
       __a       =    list(map(a   ,    input().split()				)				)

       print("enter the values of corresponding parameters: "				)
       for i in range(a				):
              __a       =    float(input()				)

       __a       =    int(input("enter the value to interpolate: "				)				)
       __a       =    (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1   ,    a				):
              for j in range(n - i				):
                     __a       =    y[j + 1][i - 1] - y[j][i - 1]

       __a       =    y[0][0]
       for i in range(1   ,    a				):
              summ += (ucal(a   ,    a				) * y[0][i]) / math.factorial(a				)

       print(F"the value at {value} is {summ}"				)


if __name__ == "__main__":
   main()
